Predicting the difficulty level faced by academic achievers based on brainwave analysis

• Main Authors: Azcarraga, Judith Jumig, Suarez, Merlin Teodosia C., Inventado, Paul Salvador B.
• Format: text
• Published: Animo Repository 2010
• Subjects: Brain—Electromechanical analogies; Academic achievement; Brain stimulation; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1269
• Institution: De La Salle University

Students who performed well in their college mathematics subjects, referred to here as academic achievers, were divided into two groups according to the self-reported level of difficulty faced by them while performing several programming tasks in LOGO - a programming language using turtle-graphics. It is shown that, to some extent, the level of difficulty of tasks faced by academic achievers can be predicted, based on their measured affective levels of excitement, frustration and engagement. These affective states are measured using brainwaves sensors that are attached to the head of the student. Those who assessed the learning experience as easy tend to have higher levels of excitement than those who reported to have experienced difficulty in learning the language. On the other hand, the level of frustration among those having difficulty with the tasks registered slightly higher frustration levels. Three machine learning algorithms were used to predict whether or not a learner finds the tasks to be easy. The average predictive accuracy is 70%.